Shape-based image retrieval of Chinese paper-cutting using RBFNN with invariant moment
- Resource Type
- Conference
- Authors
- Zheng, Timothy Tian-Ming; Ng, Wing W. Y.; Huang, Xu-Sheng; Yang, Shi-Ting; Chan, Patrick P. K.; Lai, Weiwei; Yeung, Daniel S.
- Source
- 2010 International Conference on Machine Learning and Cybernetics Machine Learning and Cybernetics (ICMLC), 2010 International Conference on. 2:808-814 Jul, 2010
- Subject
- Computing and Processing
Communication, Networking and Broadcast Technologies
Machine learning
Cybernetics
Decision support systems
Iron
Conferences
Chinese Paper-Cutting
Content-Based Image Retrieval
Invariant moments
Localized Generalization Error Model
- Language
- ISSN
- 2160-133X
2160-1348
Computer aided design (CAD) system for traditional Chinese paper-cutting provides significant assistance for folk artists creating delicate handicrafts. However, the large amount of paper-cutting patterns creates several major challenges for the management of an online pattern library. Artists could easily collect favorite pattern materials and share their creative works with others using an online pattern library. A proper image descriptor and effective content-based image retrieval (CBIR) strategy are needed to provide a resourceful online database. We propose to combine invariant moments descriptor of paper-cutting patterns and Radial Basis Function Neural Networks (RBFNN) trained by a minimization of the Localized Generalization Error (LGEM) to provide the CBIR function for paper-cutting retrieval. Experimental results show that the proposed method outperforms similarity based method.